A major operational issue identified when integrating large amounts of intermittent renewable energy resources into a power grid is that both the ramp-rate and magnitude of continuous regulation reserves (CRRs), such as regulation and load following services, are expected to increase significantly. In meeting increased ramp and capacity requirements, the regulating generators, which, in the past, were the only source of such services, may be unable to operate close to their preferred operating points, resulting in lower efficiencies. Faster regulating movements also increase mechanical stress on these generators, shortening their lifetimes and increasing the wear-and-tear cost. Emerging energy resources, such as batteries, flywheels, and demand-side management (DSM), are flexible energy options that could provide the needed fast-response ancillary services. Of these options, DSM is the most underutilized for CRRs. This is because the fast regulating service should be available whenever needed; it should be fully controllable, observable, and measurable to become a product in ancillary service market. In addition, loads must be aggregated to the MW level to be practical to bid into the ancillary service market under current market rules. Nevertheless, since May 2006, markets for regulation service from DSM programs have opened in Pennsylvania, New Jersey, the Maryland Interconnection, and the PJM Interconnection LLC (PJM), although because of the strict telemetry requirement, all the participants of these programs have been large industrial customers so far.
The two-way communication network of the smart grid infrastructure enables flexible control of end devices from utility control centers. In general, there are two control methods in DSM: indirect load control (ILC) and direct load control (DLC). In ILC, the power consumption of loads is controlled by consumers or autonomously by end devices themselves. Control signals include electricity prices, system voltage, or system frequency deviation. For example, the set point control of thermostatically controlled appliances (TCAs) according to the real-time electricity tariff affects end users less than load shedding. However, the relationship between varying numbers of the external parameters (e.g., electricity tariffs) and power consumption is very complicated. Not only is the relationship nonlinear, but the power consumption may oscillate because of the lack of load diversity after the control is initiate. Therefore, ILC is not suitable to provide CRRs under current electricity market settings.
In DLC, the load is controlled directly by a utility or a system operator, making it possible to adjust the consumption precisely. Therefore, DLC has been selected in this study to demonstrate the applications of providing CRRs using TCAs. To gain customers' acceptance, the control implementation needs to be paid well and non-intrusive. Consumer can also override the centralized control when they no longer want to participate.
CRRs are high value ancillary services compared to peak shaving and load shifting. For example, the average bi-directional regulation price of the CAISO balancing authority is $11.95/MW (January-July, 2010) and that of the BPA balancing authority is $9.38/MW (2010). The regulation prices are expected to rise continuously when renewable penetration keeps increasing. Therefore, it is possible to generate financial incentives for consumer participation.
Technical challenges to design a DLC controller that meet CRR requirements include:                When designing a central controller, one needs to consider the communication delays, errors, and bandwidth limitations.        Operational characteristics of the TCAs should be taken into consideration. For example, the TCA lifetime shall not be shortened; the quality of its service shall not be significantly degraded; the safety and the comfort settings of the consumers shall not be tampered.        The consumer override function should to be considered as a denial of service.        The controller must be robust in order to tolerant the response delays and errors from the large amount of distributed TCA resources.        
Callaway proposes a system identification approach based on Fokker-Planck diffusion models to design a direct control strategy to manage large populations of heating, ventilating, and air-conditioning (HVAC) units (See, D. S. Callaway, “Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy,” Energ. Convers. And Manag., Vol. 50 No. 9, pp. 1389-1400, 2009). An extended optimal centralized control strategy with comfort-constraints is proposed by Parkinson et al. (See, S. Parkinson, D. Wang, C. Crawford, and N. Djilali. “Comfort-constrained distributed heat pump management,” Proc. of IEEE ICSGCE 2011, 2011); Wang et al. implemented this method on a simulation test bed to investigate the regulation and load shifting service supported by HVAC units to offset the intermittency of renewable resources in a self-regulating distribution system (See, D. Wang, B. de Wit, S. Parkinson, J. Fuller, D. Chassin, C. Crawford, N. Djilali, “A Test Bed for Self-regulating Distribution Systems: Modeling Integrated Renewable Energy and Demand Response in the GridLAB-D/MATLAB Environment,” IEEE ISGT2012, IEEE PES Conference on Innovative Smart Grid Technologies, Washington Marriott Wardman Park in the District of Columbia, 2012). This control scheme requires sending a control signal to lower or raise thermostat setpoints of the HVACs based on an estimated probability density function of their on/off status and room temperatures. The drawback of controlling TCA thermostats is that the load diversity will be lost if thermostats of a group of TCA are raised or lowered frequently without coordination. In addition, the computation burden to identify aggregated TCA load dynamics is also challenging. The direct control of electric water heaters (EWHs) to adjust their power consumption to follow regulation signals has been investigated in (See, J. Kondoh, N. Lu, and D. J. Hammerstrom, “An Evaluation of the Water Heater Load Potential for Providing Regulation Service,” IEEE Trans. on Power Systems, issue: 99, 2011). Because no resource prioritization is made in the approach, the EWHs being switched on or off may not be the optimal ones to be on or off. Therefore, over 10,000 EWHs were required to provide ±1 MW regulation services.